Baselines: We use four baselines for VQAv2: 1) BUTD [1]}, an early competitive approach that used detector-based features pre-trained on VisualGenome [2]}; 2) BAN [3]}, a baseline that uses an effective multi-modal bilinear attention; 3) LMH [4]}, originally crafted for VQA-CP, this approach removes superficial question patterns; and 4) LXMERT [5]} a large-scale Transformer-based model that is pre-trained with large amounts of image-text pairs. For VQA-CP, we also analyze: 1) CSS [6]} which generates counterfactual training samples by masking critical objects in images or words in questions; 2) RMFE [7]} which maximizes the amount of information that each modality contributes to the prediction.
